Smoothed Analysis of Interior-Point Algorithms: Termination

We perform a smoothed analysis of the termination phase of an interior-point method. By combining this analysis with the smoothed analysis of Renegar’s interior-point algorithm in [DST02], we show that the smoothed complexity of an interior-point algorithm for linear programming is O(m log(m/σ)). In contrast, the best known bound on the worst-case complexity of linear programming is O(mL), where L could be as large as m. We include an introduction to smoothed analysis and a tutorial on proof techniques that have been useful in smoothed analyses.